Extraction of phenotypic traits from taxonomic descriptions for the tree of life using natural language processing
AffiliationUniv Arizona, Sch Informat, Tucson, AZ 85719 USA
MetadataShow full item record
PublisherBOTANICAL SOC AMER INC
CitationEndara, L., H. Cui, and J. G. Burleigh. 2018. Extraction of phenotypic traits from taxonomic descriptions for the tree of life using natural language processing. Applications in Plant Sciences 6(3): e1035.
JournalAPPLICATIONS IN PLANT SCIENCES
Rights© 2018 Endara et al. Applications in Plant Sciences is published by Wiley Periodicals, Inc. on behalf of the Botanical Society of America. This is an open access article under the terms of the Creative Commons Attribution License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractPremise of the StudyPhenotypic data sets are necessary to elucidate the genealogy of life, but assembling phenotypic data for taxa across the tree of life can be technically challenging and prohibitively time consuming. We describe a semi-automated protocol to facilitate and expedite the assembly of phenotypic character matrices of plants from formal taxonomic descriptions. This pipeline uses new natural language processing (NLP) techniques and a glossary of over 9000 botanical terms. Methods and ResultsOur protocol includes the Explorer of Taxon Concepts (ETC), an online application that assembles taxon-by-character matrices from taxonomic descriptions, and MatrixConverter, a Java application that enables users to evaluate and discretize the characters extracted by ETC. We demonstrate this protocol using descriptions from Araucariaceae. ConclusionsThe NLP pipeline unlocks the phenotypic data found in taxonomic descriptions and makes them usable for evolutionary analyses.
NoteOpen access journal.
VersionFinal published version
SponsorsU.S. National Science Foundation [DEB-1208256, DEB-1541506, DBI-1147266]